SOLiDzipper: A High Speed Encoding Method for the Next-Generation Sequencing Data

Background Next-generation sequencing (NGS) methods pose computational challenges of handling large volumes of data. Although cloud computing offers a potential solution to these challenges, transferring a large data set across the internet is the biggest obstacle, which may be overcome by efficient encoding methods. When encoding is used to facilitate data transfer to the cloud, the time factor is equally as important as the encoding efficiency. Moreover, to take advantage of parallel processing in cloud computing, a parallel technique to decode and split compressed data in the cloud is essential. Hence in this review, we present SOLiDzipper, a new encoding method for NGS data. Methods The basic strategy of SOLiDzipper is to divide and encode. NGS data files contain both the sequence and non-sequence information whose encoding efficiencies are different. In SOLiDzipper, encoded data are stored in binary data block that does not contain the characteristic information of a specific sequence platform, which means that data can be decoded according to a desired platform even in cases of Illumina, Solexa or Roche 454 data. Results The main calculation time using Crossbow was 173 minutes when 40 EC2 nodes were involved. In that case, an analysis preparation time of 464 minutes is required to encode data in the latest DNA compression method like G-SQZ and transmit it on a 183 Mbit/s bandwidth. However, it takes 194 minutes to encode and transmit data with SOLiDzipper under the same bandwidth conditions. These results indicate that the entire processing time can be reduced according to the encoding methods used, under the same network bandwidth conditions. Considering the limited network bandwidth, high-speed, high-efficiency encoding methods such as SOLiDzipper can make a significant contribution to higher productivity in labs seeking to take advantage of the cloud as an alternative to local computing. Availability http://szipper.dinfree.com . Academic/non-profit: Binary available for direct download at no cost. For-profit: Submit request for for-profit license from the web-site

Recombinant mussel proximal thread matrix protein promotes osteoblast cell adhesion and proliferation

BACKGROUND: von Willebrand factor (VWF) is a key load bearing domain for mamalian cell adhesion by binding various macromolecular ligands in extracellular matrix such as, collagens, elastin, and glycosaminoglycans. Interestingly, vWF like domains are also commonly found in load bearing systems of marine organisms such as in underwater adhesive of mussel and sea star, and nacre of marine abalone, and play a critical load bearing function. Recently, Proximal Thread Matrix Protein1 (PTMP1) in mussel composed of two vWF type A like domains has characterized and it is known to bind both mussel collagens and mammalian collagens. RESULTS: Here, we cloned and mass produced a recombinant PTMP1 from E. coli system after switching all the minor codons to the major codons of E. coli. Recombinant PTMP1 has an ability to enhance mouse osteoblast cell adhesion, spreading, and cell proliferation. In addition, PTMP1 showed vWF-like properties as promoting collagen expression as well as binding to collagen type I, subsequently enhanced cell viability. Consequently, we found that recombinant PTMP1 acts as a vWF domain by mediating cell adhesion, spreading, proliferation, and formation of actin cytoskeleton. CONCLUSIONS: This study suggests that both mammalian cell adhesion and marine underwater adhesion exploits a strong vWF-collagen interaction for successful wet adhesion. In addition, vWF like domains containing proteins including PTMP1 have a great potential for tissue engineering and the development of biomedical adhesives as a component for extra-cellular matrix

Survival analysis of implants after surgical treatment of peri-implantitis based on bone loss severity and surgical technique: a retrospective study

Background Few trials have compared the results of surgical treatment for peri-implantitis based on severity of peri-implantitis and surgical method. This study investigated the survival rate of implants based on type of surgical method used and initial severity of peri-implantitis. Classification of severity was determined based on bone loss rate relative to fixture length. Methods Medical records of patients who underwent peri-implantitis surgery from July 2003 to April 2021 were identified. Classification of peri-implantitis was divided into 3 groups (stage 1: bone loss  50%) and performance of resective or regenerative surgery was investigated. Kaplan-Meier survival curves and Cox hazards proportional models were used to analyze the cumulative survival rate of implants. Median survival time, predicted mean survival time, hazard ratio (HR), and 95% confidence interval (CI) were calculated. Results Based on Kaplan-Meier analysis, 89 patients and 227 implants were included, and total median postoperative survival duration was 8.96 years. Cumulative survival rates for stage 1, 2, and 3 were 70.7%, 48.9%, and 21.3%, respectively. The mean survival time for implants in stage 1, 2, and 3 was 9.95 years, 7.96 years, and 5.67 years, respectively, with statistically significant difference (log-rank p-value < 0.001). HRs for stage 2 and stage 3 were 2.25 and 4.59, respectively, with stage 1 as reference. Significant difference was not found in survival time between resective and regenerative surgery groups in any peri-implantitis stage. Conclusions The initial bone loss rate relative to the fixture length significantly correlated with the outcome after peri-implantitis surgery, demonstrating a notable difference in the long-term survival rate. Difference was not found between resective surgery and regenerative surgery in implant survival time. Bone loss rate could be utilized as a reliable diagnostic tool for evaluating prognosis after surgical treatment, regardless of surgical method used

Measuring the maturity of open access: a preliminary study

Open access is an important part of scholarly communication, and it has been a global phenomenon. The growth of open access brings several signif-icant benefits to the general public as well as researchers, ultimately leads to the advancement of science. For the continuous growth and development of open access, it is necessary to measure the degree of maturity of open ac-cess. However, there is not much discussion about the assessment frame-work for open access. This study aims to propose an assessment framework of open access maturity. For the purpose of this study, we conducted an analysis with a total of 24 literatures relevant to the digital maturity, the ma-turity of open data/open science, and major open access initiatives. For digi-tal maturity, 18 articles were analyzed: 10 articles for generic purpose model, and 8 articles for industry-specific model. In addition, three articles on the maturity of open data/open science were analyzed and three major open ac-cess initiatives. In preliminary analysis results, three dimensions with 13 be-longing items were proposed for measuring the maturity of open access. Three dimensions are OA Policy, OA capability, and Openness quality. For OA policy, there are three items such as OA policy document, OA govern-ance, and OA strategy. For OA Capability, finance for OA, people for OA, culture for OA, and collaboration for OA are proposed. For Openness Quali-ty dimension, six items are suggested: submission and review, author rights, user rights, findability, accessibility, and monitoring

Functional Characterization of Siberian Wild Rye Grass \u3cem\u3eEsHSP 16.9\u3c/em\u3e Gene Conferring Diverse Stress Tolerance in Prokaryotic Cells

Siberian wild rye (Elymus sibiricus L.) is a perennial, caespitose, and self-pollinating grass indigenous to Northern Asia and also is widely distributed from Northern Europe to Japan. The plant shows strong environmental adaptability with tolerance to drought and cold; thus, it is often used as forage resources (Yan et al., 2007). Environmental stresses caused by global warming are acknowledged to be as a serious issue in agriculture due to reductions of crop productivity (Ahuja et al., 2010). Genetic natural breeding of Siberian wild rye would potentially increase the productivity of forage crops; however, genetic studies on this grass have yet to be conducted. Heat shock proteins (Hsps) are the well characterized stress inducible proteins playing as molecular chaperones in prokaryotes and eukaryotes. We have also identified two differently localized small Hsps: rice chloroplastic and alfalfa mitochondrial Hsps confer tolerance to oxidative and heat stresses in tall fescue and to salinity and arsenic stresses in E. coli, tobacco, and tall fescue, respectively (Lee et al., 2012a; Lee et al., 2012b). Here, we cloned the small Hsp16.9 gene from various heat stress-induced fragments in Siberian wild rye using differentially expressed gene (DEG) analysis. We examined the mRNA expression of EsHsp16.9, in vitro molecular chaperone activity and in vivo stress tolerance by using a prokaryotic system against diverse environmental stresse

BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning

With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient transfer learning (PETL) of large models has grasped huge attention. While recent PETL methods showcase impressive performance, they rely on optimistic assumptions: 1) the entire parameter set of a PTM is available, and 2) a sufficiently large memory capacity for the fine-tuning is equipped. However, in most real-world applications, PTMs are served as a black-box API or proprietary software without explicit parameter accessibility. Besides, it is hard to meet a large memory requirement for modern PTMs. In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters. BlackVIP has two components; 1) Coordinator and 2) simultaneous perturbation stochastic approximation with gradient correction (SPSA-GC). The Coordinator designs input-dependent image-shaped visual prompts, which improves few-shot adaptation and robustness on distribution/location shift. SPSA-GC efficiently estimates the gradient of a target model to update Coordinator. Extensive experiments on 16 datasets demonstrate that BlackVIP enables robust adaptation to diverse domains without accessing PTMs' parameters, with minimal memory requirements. Code: \url{https://github.com/changdaeoh/BlackVIP}Comment: Accepted to CVPR 202

Genetic Mechanisms in Aspirin-Exacerbated Respiratory Disease

Aspirin-exacerbated respiratory disease (AERD) refers to the development of bronchoconstriction in asthmatics following the exposure to aspirin or other nonsteroidal anti-inflammatory drugs. The key pathogenic mechanisms associated with AERD are the overproduction of cysteinyl leukotrienes (CysLTs) and increased CysLTR1 expression in the airway mucosa and decreased lipoxin and PGE2 synthesis. Genetic studies have suggested a role for variability of genes in disease susceptibility and the response to medication. Potential genetic biomarkers contributing to the AERD phenotype include HLA-DPB1, LTC4S, ALOX5, CYSLT, PGE2, TBXA2R, TBX21, MS4A2, IL10, ACE, IL13, KIF3A, SLC22A2, CEP68, PTGER, and CRTH2 and a four-locus SNP set composed of B2ADR, CCR3, CysLTR1, and FCER1B. Future areas of investigation need to focus on comprehensive approaches to identifying biomarkers for early diagnosis